Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of t...

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Autores principales:	Bettina Knapp, Lars Kaderali
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f9449206da6142209436d75ab561ace2
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spelling	oai:doaj.org-article:f9449206da6142209436d75ab561ace22021-11-18T09:02:03ZReconstruction of cellular signal transduction networks using perturbation assays and linear programming.1932-620310.1371/journal.pone.0069220https://doaj.org/article/f9449206da6142209436d75ab561ace22013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23935958/?tool=EBIhttps://doaj.org/toc/1932-6203Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+) T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.Bettina KnappLars KaderaliPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e69220 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bettina Knapp Lars Kaderali Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.
description	Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+) T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.
format	article
author	Bettina Knapp Lars Kaderali
author_facet	Bettina Knapp Lars Kaderali
author_sort	Bettina Knapp
title	Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.
title_short	Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.
title_full	Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.
title_fullStr	Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.
title_full_unstemmed	Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.
title_sort	reconstruction of cellular signal transduction networks using perturbation assays and linear programming.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/f9449206da6142209436d75ab561ace2
work_keys_str_mv	AT bettinaknapp reconstructionofcellularsignaltransductionnetworksusingperturbationassaysandlinearprogramming AT larskaderali reconstructionofcellularsignaltransductionnetworksusingperturbationassaysandlinearprogramming
_version_	1718421060418797568

Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

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